ArticleCancer communications (London, England)2020
Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram.
Article in Cancer communications (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 2 of them syntheses that pooled it.
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Who cites it
90 citing papers in PubMed, 2 syntheses or guidelines pooled it, 105 citations in OpenAlex.
- Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer.European radiology · 2025Pooled it
- The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Radiomics signature based on robust features derived from diffusion data for differentiation between benign and malignant solitary pulmonary lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Trial
- Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign.BMC cancer · 2023Trial
- A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules.European radiology · 2022Trial
- Radiomics machine learning models for lung cancer early diagnosis in heterogenous multicentre chest CT data: LIBRA study results.European radiology experimental · 2026Article
- Diagnostic performance of commercial AI systems versus participating radiologists for pulmonary nodule detection in routine clinical practice.Japanese journal of radiology · 2026Article
- Web-based dynamic nomogram for forecasting overall survival and cancer-specific survival among individuals with lymph node-negative pancreatic cancer: based upon the SEER database.Clinics (Sao Paulo, Brazil) · 2026Article
- Predicting Overall Survival in Patients With Multiple Primary Lung Cancer: Nomogram Development and Validation Study.JMIR cancer · 2026Article
- Radiomics analysis of CT imaging for predicting MIT family translocation renal cell carcinoma: a multicenter retrospective clinical study.BMC cancer · 2026Article
- Development and internal validation of a clinical nomogram incorporating quantitative CT features for predicting malignancy in pulmonary nodules ≤ 3 cm.BMC medical imaging · 2026Article
- Application of SHAP visualization and nomogram based on PET/CT radiomics combined models for risk stratification in high-grade invasive lung adenocarcinoma.Journal of translational medicine · 2026Article
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Stacked CT radiomics, deep learning and clinical feature models for differentiating benign and malignant solitary pulmonary nodules.Scientific reports · 2026Article
- Grayscale ultrasound radiomics for characterizing subpleural pulmonary lesions: a multicenter prospective study.Insights into imaging · 2026Article
- Review
- Differential diagnosis of benign lesions and lung adenocarcinoma presenting as lung-RADS 2022 category 4B solid nodules based on multiscale CT radiomics.BMC cancer · 2026Article
- Impact of CT acquisition settings on the stability of radiomic features and the performance of pulmonary nodule classification models.Insights into imaging · 2026Article
- Diagnostic performance of artificial intelligence models for pulmonary nodule classification: a multi-model evaluation.European radiology · 2026Article
- AI-assisted clinico-quantitative imaging nomogram for preoperative malignancy risk in solid and part-solid pulmonary nodules ≤ 3 cm: development and internal validation.Frontiers in oncology · 2026Article
30 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer is the most commonly diagnosed cancer worldwide. Its survival rate can be significantly improved by early screening. Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer. In this study, we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules.
methodsA retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography (CT) examinations between June 2013 and June 2018. We assigned 612 patients to a training cohort and 263 patients to a validation cohort. Radiomics features were extracted from the CT images of each patient. Least absolute shrinkage and selection operator (LASSO) was used for radiomics feature selection and radiomics score calculation. Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram. Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model. The performance of the radiomics nomogram was evaluated by the area under the curve (AUC), calibration curve and Hosmer-Lemeshow test in both the training and validation cohorts.
resultsA radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort. The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients' age. Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort (AUC, 0.836; 95% confidence interval [CI]: 0.793-0.879) and validation cohort (AUC, 0.809; 95% CI: 0.745-0.872). The Hosmer-Lemeshow test also showed good performance for the logistic regression model in the training cohort (P = 0.765) and validation cohort (P = 0.064). Good alignment with the calibration curve indicated the good performance of the nomogram.
conclusionsThe established radiomics nomogram is a noninvasive preoperative prediction tool for malignant pulmonary nodule diagnosis. Validation revealed that this nomogram exhibited excellent discrimination and calibration capacities, suggesting its clinical utility in the early screening of lung cancer.
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